End-to-End United Video Dehazing and Detection

The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video

Research on Dynamic Modeling and Application of Kinetic Contact Interface in Machine Tool

A method is presented which is a kind of combining theoretic analysis and experiment to obtain the equivalent dynamic parameters of linear guideway through four steps in detail. From statics analysis, vibration model analysis, dynamic experiment, and parameter identification, the dynamic modeling of linear guideway is synthetically studied. Based on contact mechanics and elastic mechanics, the mathematic vibration model and the expressions of basic mode frequency are deduced. Then, equivalent stiffness and damping of guideway are obtained in virtue of single-freedom-degree mode fitting method. Moreover, the investigation above is applied in a certain gantry-type machining center; and through comparing with simulation model and experiment results, both availability and correctness are validated

Supervised local descriptor learning for human action recognition

Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE) to learn compact but highly discriminative local feature descriptors for more accurate and efficient action recognition. By leveraging the advantages of the I2C distance, the proposed I2CDDE incorporates class labels to enable fully supervised learning of local feature descriptors, which achieves highly discriminative but compact local descriptors. The objective of our I2CDDE is to minimize the I2C distances from samples to their corresponding classes while maximizing the I2C distances to the other classes in the low-dimensional space. To further improve the performance, we propose incorporating a manifold regularization based on the graph Laplacian into the objective function, which can enhance the smoothness of the embedding by extracting the local intrinsic geometrical structure. The proposed I2CDDE for the first time achieves fully supervised learning of local feature descriptors. It significantly improves the performance of I2C-based methods by increasing the discriminative ability of local features while greatly reducing the computational burden by dimensionality reduction to handle large-scale data. We apply the proposed I2CDDE algorithm to human action recognition on four widely used benchmark datasets. The results have shown that I2CDDE can significantly improve I2C-based classifiers and achieves state-of-the-art performance

(4RS)-Methyl 4-cyano-4-cyclo­hexyl-4-phenyl­butano­ate

In the crystal structure of the title compound, C18H23NO2, there are only van der Waals inter­actions present. The cyclo­hexyl ring has a chair conformation. The longer axes of the displacement parameters of the non-H atoms forming the ethyl­methyl­carboxyl­ate skeleton are perpendicular to the plane through the non-H atoms of this skeleton

Prediction of Ebolavirus Genomes Encoded MicroRNA-Like Small RNAs Using Bioinformatics Approaches

Recent findings revealed that certain viruses encoded microRNA-like small RNAs using the RNA interference machinery in the host cells. However, the function of these virus-encoded microRNA-like small RNAs remained unclear, and whether these microRNA-like small RNAs were involved in the replication of the virus and viral infection was still disputable. In this chapter, the negative-sense RNA genome of Ebola virus (EBOV) was scanned using bioinformatics tools to predict the EBOV-encoded microRNA-like small RNAs. Then, the potential influence of viral microRNA-like small RNAs on the viral immune evasion, host cellular signaling pathway, and epigenetic regulation of antiviral defense mechanism were also detected by the reconstructed regulatory network of target genes associated with viral encoded microRNA-like small RNAs. In this analysis, EBOV-encoded microRNA-like small RNAs were proposed to inhibit the host immune response factors, probably facilitating the evasion of EBOV from the host defense mechanisms. In conclusion, systematic investigation of microRNA-like small RNAs in EBOV genome may shed light on the underlying molecular mechanisms of the pathological process of Ebola virus disease (EVD)

Urinary Metabolomics on the Biochemical Profiles in Diet-Induced Hyperlipidemia Rat Using Ultraperformance Liquid Chromatography Coupled with Quadrupole Time-of-Flight SYNAPT High-Definition Mass Spectrometry

Ultraperformance liquid chromatography coupled with quadrupole time-of-flight synapt high-definition mass spectrometry metabolomics was used to characterize the urinary metabolic profiling of diet-induced hyperlipidaemia in a rat model. Analysis was done by orthogonal partial least squares discriminant analysis, correlation analysis, heat map analysis, and KEGG pathways analysis. Potential biomarkers were chosen by S-plot and were identified by accurate mass, isotopic pattern, and MS/MS fragments information. Significant differences in fatty acid, amino acid, nucleoside, and bile acid were observed, indicating the perturbations of fatty acid, amino acid, nucleoside, and bile acid metabolisms in diet-induced hyperlipidaemia rats. This study provides further insight into the metabolic profiling across a wide range of biochemical pathways in response to diet-induced hyperlipidaemia

An Emotional Skills Intervention for Elementary Children with Autism in China: A Pilot Study

The purpose of this pilot study was to examine the effects of an emotional skills intervention on behavioral and emotional competence, as well as on communication for children with autism in China. Eight children (seven boys and one girl), aged 7 to 8, participated in this study. We used a pre and posttest group design. The intervention consisted of 10 group sessions and four individual sessions. Each group session had two or three children. The intervention curriculum consisted of emotion recognition, emotion recognition within context, self-expression of emotions, seeking help when encountering problems, and techniques for emotion regulation. Results indicated that the intervention significantly improved children’s emotional skills, behavioral and emotional competence, and communication. The potential implications of this study for elementary children with autism in China are also discussed

Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution.

We designed an epi-illumination SPIM system that uses a single objective and has a sample interface identical to that of an inverted fluorescence microscope with no additional reflection elements. It achieves subcellular resolution and single-molecule sensitivity, and is compatible with common biological sample holders, including multi-well plates. We demonstrated multicolor fast volumetric imaging, single-molecule localization microscopy, parallel imaging of 16 cell lines and parallel recording of cellular responses to perturbations